Image processing apparatus

ABSTRACT

An image processing apparatus enhances contrast without producing color balance distortion or color collapse by generating a gradation-scale correction table from a distribution of a characteristic quantity of an input image signal, using the gradation-scale correction table to derive a gradation correction parameter for each pixel from a maximum component value of the pixel, and multiplying all components of the pixel by the gradation correction parameter.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an image processing apparatus.

2. Description of the Related Art

A conventional image display apparatus disclosed by Nakahigashi et al.in Japanese Patent Application Laid-open No. 2004-342030 improves thecontrast of an input signal with components representing values of thethree primary colors red, green, and blue by generating a luminancesignal from the input signal, generating a gradation-scale correctiontable from the luminance signal, and multiplying the red, green, andblue component values by a ratio derived from the gradation-scalecorrection table.

There are two general methods for improving the contrast of a videosignal. One general method is to separate the video signal into aluminance component and a color component (e.g., a luminance signal anda pair of color difference signals) and perform a gradation correctionon the luminance component. In this method, there is a tendency forincreased luminance levels to produce faint colors (colors with lowsaturation) and reduced luminance levels to produce deep colors (withhigh saturation). To prevent this occurrence, a gradation correction issometimes also performed on the color signal (color difference signals),but this may cause the problem of color collapse, in which differencesbetween color gradation levels are lost.

The other general method is to perform the gradation correction on theprimary color components of the image signal, normally red, green, andblue, typically using the same gradation-scale correction tableindependently for each color component. A problem with this method isthat it distorts color balance, because the different primary colorcomponents of each pixel tend to be corrected by different amounts.

The method disclosed by Nakahigashi et al. combines both of thesegeneral methods. By multiplying each of the primary color values of apicture element (pixel) by the same ratio (correction parameter) itavoids distortion of color balance, but since the correction parameteris derived from luminance information, the corrected value of a colorcomponent may greatly overshoot the maximum gradation limit, causingsevere color collapse.

SUMMARY OF THE INVENTION

An object of the present invention is to improve contrast withoutdistorting color balance and without causing severe color collapse.

The invention provides an image processing apparatus for processing animage signal representing a plurality of pixels. The image signalincludes a plurality of component values for each pixel.

A maximum component value detector detects, for each pixel, a maximumvalue among the plurality of component values.

A distribution extractor extracts a distribution from the input imagesignal over a predetermined period.

A gradation-scale correction table generator generates a gradation-scalecorrection table from the distribution. The gradation-scale correctiontable represents a gradation correction parameter decision curve.

By referring to the gradation-scale correction table, a gradationcorrection parameter calculator calculates a correction parameter foreach pixel from the maximum component value of the pixel.

A multiplier multiplies the component values of the pixel by thecorrection parameter.

Distortion of color balance is prevented by using the same correctionparameter for all component values of each pixel.

Color collapse is prevented by deriving the correction parameter of apixel from the maximum component value of the pixel, so that thecorrection parameter does not cause any component value of the pixel toovershoot the maximum value on the gradation correction parameterdecision curve.

BRIEF DESCRIPTION OF THE DRAWINGS

In the attached drawings:

FIG. 1 is a block diagram illustrating the structure of an imageprocessing apparatus according to Embodiment 1 of the invention;

FIG. 2 is a graph illustrating an exemplary distribution ofcharacteristic quantities detected in the image processing apparatus inEmbodiment 1;

FIG. 3 is a graph shows an exemplary gradation correction parameterdecision curve generated in the image processing apparatus in Embodiment1;

FIG. 4 is a graph illustrating an exemplary computation of gradationcorrection parameters in the image processing apparatus in Embodiment 1;

FIG. 5 is a graph illustrating pre-gradation-correction andpost-gradation-correction gradation levels in the image processingapparatus in Embodiment 1;

FIG. 6 is a graph showing another gradation correction parameterdecision curve usable in Embodiment 1;

FIG. 7 is a graph showing still another gradation correction parameterdecision curve usable in Embodiment 1;

FIG. 8 is a graph showing yet another gradation correction parameterdecision curve usable in Embodiment 1; and

FIG. 9 is a block diagram illustrating the structure of an imageprocessing apparatus according to Embodiment 2.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the invention will now be described with reference to theattached drawings, in which like elements are indicated by likereference characters.

Embodiment 1

Referring to FIG. 1, the image processing apparatus includes inputterminals 10R, 10G, 10B, a characteristic quantity calculator 11, acharacteristic quantity distribution extractor 12, a gradation-scalecorrection table generator 13, a maximum value detector 14, a gradationcorrection parameter calculator 15, multipliers 16R, 16G, 16B, andoutput terminals 17R, 17G, 17B.

The input terminals 10R, 10G, 10B receive an image signal in aprescribed format used in television and computers. The image signal isdivided into frames, each representing one image or screen of, forexample, a moving picture. The image signal has a plurality ofcomponents. In the present example, these components are red, green, andblue primary color components.

For each pixel represented by the image signal, the characteristicquantity calculator 11 calculates a characteristic quantity from theprimary color components RI, GI, BI of the pixel as received at theinput terminals 10R, 10G, 10B and outputs the result to thecharacteristic quantity distribution extractor 12.

The characteristic quantity distribution extractor 12 extracts adistribution from the characteristic quantities received from thecharacteristic quantity calculator 11 over one frame. The extracteddistribution is supplied to the gradation-scale correction tablegenerator 13.

The following description will proceed on the assumption that thecharacteristic quantity is the luminance YI. The characteristic quantitycalculator 11 calculates the luminance YI(p) of each pixel (p), and thecharacteristic quantity distribution extractor 12 extracts the luminancedistribution DIST(YI).

From the luminance distribution DIST(YI) obtained by the characteristicquantity distribution extractor 12, the gradation-scale correction tablegenerator 13 generates a gradation-scale correction table storing datarepresenting a gradation correction parameter decision curve. Thegradation-scale correction table may be updated, for example, once perframe. Each time the characteristic quantity distribution extractor 12extracts the distribution over one frame, the gradation-scale correctiontable generator 13 generates a gradation-scale correction table for thatframe.

The gradation correction parameter decision curve plots the relationshipbetween the characteristic quantity before and after the gradationcorrection that will be applied. This curve might therefore be termed acharacteristic quantity gradation correction curve, but in the presentinvention, the curve is used to correct signal values (such as thecomponent values of the input image signal) other than thecharacteristic quantity, so the term ‘gradation correction parameterdecision curve’ is more appropriate.

The gradation-scale correction table stores corrected values of thecharacteristic quantity as data at addresses given by the uncorrectedvalues of the characteristic quantity.

In this embodiment, the gradation correction parameter decision curvehas a comparatively steep slope in any gradation range where theluminance YI distribution has a comparatively high density.

The maximum value detector 14 detects a maximum component value for eachpixel. The maximum component value is the maximum one of the input imagesignal component values of the pixel. In the example under discussion,the maximum value detector 14 detects the maximum one of the threeprimary color signal values RI(p), GI(p), BI(p) for each pixel input tothe input terminals 10R, 10G, 10B in real time and outputs the result asthe maximum component value MI(p).

If the gradation values RI(p), GI(p), BI(p) of the red, green, and blueprimary color signals of a pixel (p) are 10, 20, and 30, for example,(RI(p)=10, GI(p)=20, BI(p)=10), the maximum value detector 14 outputsMAX(RI(p), GI(p), BI(p))=30 as the maximum component value MI(p).

The detected maximum component value MI(p) is supplied to the gradationcorrection parameter calculator 15. Using the maximum component valueMI(p), the gradation correction parameter calculator 15 calculates agradation correction parameter (correction coefficient) HC(p) byreferring to the gradation-scale correction table generated in thegradation-scale correction table generator 13 and outputs the result tothe multipliers 16R, 16G, 16B.

The multipliers 16R, 16C, and 16B multiply the red, green, and blueprimary color signal values RI(p), GI(p), BI(p) by the correctioncoefficient HC(p) calculated by the gradation correction parametercalculator 15, and output the results to the output terminals 17R, 17G,17B.

A more specific example of the image processing apparatus in FIG. 1 willnow be described in detail.

The characteristic quantity calculator 11 calculates a luminance valueYI(p) from the primary signal values RI(p), GI(p), BI(p) of each pixelin real time and outputs the result. The luminance value may becalculated using a generally known equation.

If one screen (frame) has one million pixels, for example, thecharacteristic quantity distribution extractor 12 constructs a histogramof the luminance gradation values YI of the one million pixels, andoutputs the extracted result DIST(YI). The histogram may indicate thefrequency of each gradation level (showing 256 frequencies if there are256 gradation levels, or 1024 frequencies if there are 1024 gradationlevels); alternatively, the histogram may indicate frequencies ofclasses each consisting of a plurality of gradation levels. For example,frequencies may be obtained for sixteen or thirty-two classes ofgradation levels. FIG. 2 shows an exemplary histogram that divides 1024gradation levels into sixteen classes of sixty-four levels each. Thesixteen frequency values (CY) constitute the extracted distributionDIST(YI).

The gradation-scale correction table generator 13 converts thecharacteristic distribution DIST(YI) received from the characteristicquantity distribution extractor 12 to a table of data representing thegradation correction parameter decision curve.

FIG. 3 shows an exemplary gradation correction parameter decision curvederived from the sixteen-class histogram in FIG. 2. The horizontal axisof the graph in FIG. 3 indicates gradation values (luminance gradationvalues YI) of the input characteristic quantity. The vertical axisindicates the frequency NCY of each class, normalized so that the totalfrequency of the sixteen classes is equal to the maximum gradation (1023in this example). The curve above the frequency bars indicates thecumulative frequency YO, which is obtained by summing the normalizedfrequency values from left to right, that is, from the minimum class tothe maximum class. The curve is shown as a broken line or polylinestarting at zero in the bottom left and ending at the maximum value(1023) in the top right.

The broken line is plotted by linking points with vertical coordinatesrepresenting the cumulative sum at each class and horizontal coordinatesrepresenting the maximum value in the class. This broken line is thegradation correction parameter decision curve.

The normalization is performed by multiplying the frequencies orcumulative sums by the maximum value of the gradation range divided bythe number of pixels in one frame (in this example, 1023/1,000,000).

The gradation correction parameter decision curve plotted in this wayhas a comparatively steep slope at classes or in gradation regions withcomparatively high frequencies, where the distribution of thecharacteristic quantity has a comparatively high density, and has a moregentle slope at classes or in gradation regions where the distributionof the characteristic quantity has a comparatively low density. Providedthis condition is satisfied, the gradation correction parameter decisioncurve may be plotted by a method other than the method described above.

The gradation correction parameter decision curve may be limited bysetting a lower limit or an upper limit on the slope of the curve. Ifthis is done, then each change in the curve made to conform to the slopelimit should be balanced by a compensating change in another part of thecurve, where the slope is already within the limit, so that the curvestill ends at a cumulative sum equal to the maximum gradation value(1023). In other words, if the frequency of a class is increased ordecreased to conform to the limit, a compensating frequency adjustmentshould be made in another class or other classes.

The gradation correction parameter calculator 15 receives from thegradation-scale correction table generator 13 a table of datarepresenting the gradation correction parameter decision curve andobtains a correction parameter HC(p) for each pixel. In the example inFIG. 3, the table consists of sixteen cumulative sums representing thecumulative histogram.

The multipliers 16R, 16G, 16B respectively multiply the red, green, andblue primary color signal values RI(p), GI(p), BI(p), for example,received at the input terminals 10R, 10G, 10B by the correctioncoefficient HC(p), and output the resulting corrected signals RO(p),GO(p), BO(p).

An exemplary method of calculating correction coefficients HC(p) in thegradation correction parameter calculator 15 will be described withreference to FIG. 4. In FIG. 4, the horizontal axis indicatespre-correction luminance gradation values YI, and the vertical axisindicates post-correction luminance gradation values YO. The curveextending from the lower left to the upper right is the same gradationcorrection parameter decision curve as in FIG. 3.

The horizontal coordinate values YI1, YI2, . . . , YI16 representaddress values in the gradation-scale correction table; the verticalcoordinate values YO1 YO2, . . . , YO16, represent the correspondingvalues on the gradation correction parameter decision curve, and arestored in the gradation-scale correction table at these addresses.

When the gradation correction parameter calculator 15 receives themaximum component value MI(p) of a pixel (p) from the maximum valuedetector 14, it uses the maximum component value MI(p) to obtain acorrection parameter from the curve in FIG. 4.

If MI(p) equals one of the sixteen YIn values (YI1 to YI16 in FIG. 4),the gradation correction parameter calculator 15 reads the correspondingYOn value from the table generated by the gradation-scale correctiontable generator 13, and outputs the ratio of YOn to YIn as thecorrection coefficient HC(p). That is,HC(p)=HCn=YOn/YIn

If MI(p) is not equal to any YIn, then MI(p) is situated between twoconsecutive values YIa and YIb, where b is an integer from 1 to 16 and ais the next lower integer (a=b−1). That is, NI(p) belongs to the classwith maximum value being YIb. The gradation correction parametercalculator 15 outputs the value given by equation (1) below as thecorrection coefficient HC(p) in this case.HC(p)=HCa×{MI(p)−YIa}/{YIb−YIa}+HCb×{YIb−MI(p)}/{YIb−YIa}  (1)

Equation (1) indicates that HC(p) is obtained by linear interpolationbetween the correction parameter HCb (=YOb/YIb) for the maximum valueYIb in the class to which the maximum component value MI(p) belongs andthe correction parameter HCa (=YOa/YIa) for the maximum value YIa in thenext lower class, according to the difference between MI(p) and YIa andthe difference between MI(p) and YIb.

That is, if MI(p) is between YIa and YIb as shown in FIG. 4, the slopeof the line connecting the origin (0, 0) to the point (MI(p), MO(p))obtained by internally dividing the line connecting point (YIa, YOa) andpoint (YIb, YOb) in the ratio of (MI(p)−YIa) to (YIb−MI(p)) is obtainedby using the slope HCa (=YOa/YIa) at point (YIa, YOa) and the slope HCb(=YOb/YIb) at point (YIb, YOb), and the result is output as thecorrection coefficient HC(p).

Linear interpolation of the slopes HCa and HCb does not yield the exactslope of the line joining the origin (0, 0) to point (MI(p), MO(p));there is a slight error, but the error is too small to be significant.

If the classes have a constant width, as described above, then when theslope is obtained by interpolation between the maximum value in a classand the maximum value of the adjacent class one level lower, that is,between the slopes (HCb, HCa) at the endpoints of the class, since thedivisor used to calculate the slopes HCb and HCa (in the example above,(YIb−YIa)) has a fixed value, the circuit size can be reduced by using amultiplier or shifter instead of a divider. In particular, if the fixedvalue is a power of two (2^(m), where m is an integer equal to orgreater than 2), division can be replaced by bit shifting.

Instead of using equation (1) to interpolate the slope HC(p) between theslopes HCb and HCa, the value of MO(p) may be interpolated between thevertical coordinate values YOb and YOa, and the ratio of MO(p) to MI(p)may be output as the correction coefficient HC(p). In this case there isno error.

The operation for obtaining MO(p) by interpolation from YOb and YOa isgiven by the following equation (2).MO(p)=YOa×{MI(p)−YIa}/{YIb−YIa}+YOb×{YIb−MI(p)}/{YIb−YIa}  (2)

The operation for calculating the correction coefficient HC(p) fromMO(p) and MI(p) is given by the following equation (3).HC(p)=MO(p)/MI(p)  (3)

The correction coefficient HC(p) obtained by either of the methodsdescribed above from the maximum component value MAX(RI(p), GI(p),BI(p))=CI(p) of the red, green, and blue primary color signals RI(p),GI(p), BI(p) for each pixel is supplied to the multipliers 16R, 16G,16B, where the exemplary red, green, and blue primary color signalsRI(p), GI(p), BI(p) input from the input terminals 10R, 10G, 10B aremultiplied by the common correction coefficient HC(p).

The effect of the present invention will be described with reference toFIG. 5. The horizontal axis indicates luminance gradation YI and thegradation values of the red, green, and blue primary color signals RI,GI, and BI; the vertical axis indicates post-correction luminancegradation YO and the post-correction gradation values of the red, green,and blue primary color signals RO, GO, and BO. The solid broken linedrawn from the lower left to the upper right is the gradation correctionparameter decision curve. To make the effect of the invention moreclear, a curve slightly different from the gradation correctionparameter decision curve in FIG. 4 is shown.

The symbols RI(p), GI(p), BI(p) on the horizontal axis indicate thegradation values of the red, green, and blue signals for a given pixel(p) input from the input terminals 10R, 10G, 10B. YI(p) is the luminancegradation value calculated from the red, green, and blue gradationvalues RI (p), GI (p), and BI (p).

The maximum component value MAX(RI(p), GI(p), BI(p))=MI(p) for the givenpixel (p) is BI(p). The correction parameter (correction coefficient)HC(p) is the ratio of the vertical coordinate MO(p) to the horizontalcoordinate MI(p) of the gradation correction parameter decision curve atthis horizontal coordinate value MI(p). The multipliers 16R, 16G, 16Bmultiply each of the primary color signal values RI(p), GI(p), BI(p) ofthe given pixel (p) by this correction coefficient HC(p).

The post-correction signal value BO(p) obtained by multiplying BI(p) bythe correction coefficient HC(p) is equal to MO(p) and therefore cannotexceed the maximum gradation value (1023). The post-correction signalvalues RO(p) and GO(p) are obtained by multiplying RI(p) and GI(p) whichare smaller than BI(p), by the same correction coefficient HC(p), sothey are smaller than BI(p) and they also cannot exceed the maximumgradation value (1023).RO(p)=HC(p)×RI(p)GI(p)=HC(p)×GI(p)

If the ratio YO(p)/YI(p) of the vertical coordinate YO(p) to thehorizontal coordinate YI(p) of the gradation correction parameterdecision curve at the horizontal coordinate value equal to the luminancevalue YI(p) of the pixel is used as the correction coefficient for thepixel as in the prior art, multiplication of the pixel component valuesRI(p), GI(p), BI(p) by the ratio YO(p)/YI(p) yields the corrected valuesRYO(p), GYO(p), BYO(p) indicated by dotted lines. The BYO(p) valueexceeds the maximum gradation value 1023. This results in colorcollapse, because values exceeding the maximum gradation value 1023 arelimited to the maximum gradation value 1023 by a clipping circuit.

In Embodiment 1, since the gradation-scale correction table isconstructed from the luminance distribution of the pixels in one frame,if the frame has generally low luminance, the gradation correctionproduces a brighter image with higher contrast. If the frame hasgenerally high luminance, the gradation correction produces ahigher-contrast image with crisper black colors.

Although the gradation-scale correction table used in the above exampleis generated from the luminance distribution, a similar effect can beproduced if the gradation-scale correction table is generated from anyother characteristic quantity. Since the gradation-scale correctiontable is read at the maximum component value for each pixel to obtainthe gradation correction parameter for that pixel from the maximumgradation correction parameter decision curve, the post-correction colorcomponent values of the pixel never exceed the maximum gradation value,and no color collapse occurs.

The same effect can also be produced when the image signal hascomponents other than the red, green, blue primary color components. Ifthe correction parameter for each pixel is obtained by reading thegradation correction parameter decision curve at the maximum componentvalue of the pixel, the corrected component values will always be withinthe maximum gradation value, and the gradation range will be usedeffectively without color collapse or white collapse.

In Embodiment 1, the unaltered cumulative histogram of thecharacteristic quantity is used as the gradation correction parameterdecision curve to create the gradation-scale correction table, but it isalso possible to generate the gradation correction parameter decisioncurve by altering the cumulative histogram values. The slope limitationmentioned above is one exemplary alteration. Other exemplary alterationswill be described below.

As shown in FIG. 6, an image with high average illumination, forexample, can be corrected to produce crisper black colors by reducingthe values of the gradation correction parameter decision curve in thelowest gradation region.

If the values of the gradation correction parameter decision curve inthe highest gradation region are increased as shown in FIG. 7, the imagecan be brightened. In this case, some output values may exceed themaximum gradation value (1023), and may have to be clipped to themaximum gradation value (1023), causing slight color collapse, but thelarge color collapse seen in the prior art (e.g., BYO(p) in FIG. 5) willnever occur. Compared to the advantage of being able to obtainconsistently brighter images, the disadvantage of producing a slightcolor collapse or white collapse (in which bright white colors aredisplayed with no gradation difference) is negligible. In practice, thisalteration has a large beneficial effect.

It is also possible to simplify the gradation-scale correction table asshown in FIG. 8, by plotting the gradation correction parameter decisioncurve as a broken line consisting of three straight segments. Onesegment connects a first point (MNI, MNO) to a second point (MXI, MXO).Another segment connects the first point (MNI, MNO) to the origin (0,0). The third segment connects the second point (MXI, MXO) to themaximum point (1023, 1023). The horizontal coordinate MNI of the firstpoint may be the minimum gradation value of the pixels in one screen orframe, or an equivalent value; the vertical coordinate MNO of the firstpoint is the corresponding post-correction value. The horizontalcoordinate value MXI of the second point may be the maximum gradationvalue of the pixels in the one screen or frame, or an equivalent value;the vertical axis coordinate value MXO of the second point is thecorresponding post-correction value. The maximum horizontal and verticalcoordinates are the pre-correction maximum gradation value (1023) andthe corresponding post-correction gradation value (1023).

In FIG. 8, MNO is set to substantially one-sixteenth of the maximumvalue, and MNI is selected so that one-sixteenth of the pixels in theframe have luminance values equal to or less than MNI, as determinedfrom the luminance histogram distribution. Similarly, MXO is set tosubstantially fifteen-sixteenths of the maximum value, and MXI isselected so that one-sixteenth of the pixels in the frame have luminancevalues equal to or greater than MXI.

This simplification improves contrast while reducing circuit size.

Embodiment 2

The image processing apparatus according to Embodiment 2 will now bedescribed with reference to FIG. 9. The structure of the imageprocessing apparatus differs from the image processing apparatus in FIG.1 only in that the characteristic quantity calculator 11 andcharacteristic quantity distribution extractor 12 are replaced with amaximum value distribution extractor 18.

In the image processing apparatus in FIG. 9, the maximum value detector14 can be regarded as performing the functions of the characteristicquantity calculator 11 in FIG. 1, and the maximum value distributionextractor 18 can be regarded as an instance of the characteristicquantity distribution extractor 12 in FIG. 1. The characteristicquantity calculator 11 in Embodiment 1 calculates an arbitrarycharacteristic quantity from the primary color signals for each pixeland the characteristic quantity distribution extractor 12 extracts thedistribution of the characteristic quantity. Luminance was used as anexemplary characteristic quantity in the description of Embodiment 1. InEmbodiment 2, the maximum component value MAX(RI(p), GI(p), BI(p))=MI(p)of the input primary color signals RI(p), GI(p), BI(p) for each pixel(p) is used as the characteristic quantity. The maximum component valuefor each pixel is determined by the maximum value detector 14, whichtherefore also functions as (is equivalent to) the characteristicquantity calculator.

The maximum component value MI(p) detected by the maximum value detector14 is supplied to the maximum value distribution extractor 18 and thegradation correction parameter calculator 15.

The maximum value distribution extractor 18 extracts the distributionDIST(MI) of the maximum component value MI(p) of each input pixel overone screen (one frame). The extracted distribution is supplied to thegradation-scale correction table generator 13.

From the distribution DIST(MI) of maximum component values, thegradation-scale correction table generator 13 generates agradation-scale correction table that stores data representing agradation correction parameter decision curve. The data are stored ataddresses representing maximum component values MI(p).

The gradation correction parameter decision curve relates the maximumcomponent values MI(p) of the pixels before gradation correction to themaximum component values MO(p) of the pixels after the gradationcorrection.

The gradation-scale correction table generator 13, the gradationcorrection parameter calculator 15, and the multipliers 16R, 16G, 16Bhave the same functions as in Embodiment 1.

Embodiment 2 produces effects similar to the effects of Embodiment 1.

In addition, since the maximum component value is used as thecharacteristic quantity, the gradation range can be exploited morefully.

More specifically, if the correction parameters are determined by agradation correction parameter decision curve plotted from the maximumcomponent values in Embodiment 2, and from a characteristic quantity(such as luminance) other than the maximum component value in Embodiment1, then the correction parameters will attain larger values inEmbodiment 2 than in Embodiment 1, producing greater post-correctiongradation values, while still avoiding color collapse.

Because the maximum value detector 14 also operates as thecharacteristic quantity calculator, Embodiment 2 has a simpler circuitconfiguration than Embodiment 1.

Embodiment 2 also permits variations similar to those described inEmbodiment 1.

Embodiments 1 and 2 have been described in relation to a ten-bit digitalsignal providing 1024 gradation levels from 0 to 1023, but the number ofthe gradation levels is not limited to this value.

Those skilled in the art will recognize that further variations arepossible within the scope of the invention, which is defined in theappended claims.

1. An image processing apparatus for processing an image signalrepresenting a plurality of pixels, the image signal having a pluralityof component values for each pixel, the image processing apparatuscomprising: a maximum component value detector for detecting, for eachpixel, a maximum value among the plurality of component values of theinput image signal; a distribution extractor for extracting adistribution from the input image signal over a predetermined period; agradation-scale correction table generator for generating agradation-scale correction table from the distribution, thegradation-scale correction table representing a gradation correctionparameter decision curve; a gradation correction parameter calculatorfor calculating a gradation-scale correction parameter for each pixelfrom the maximum component value of the pixel by referring to thegradation-scale correction table; and a multiplier for multiplying theplurality of component values of the pixel by the gradation correctionparameter.
 2. The image processing apparatus of claim 1, wherein thegradation correction parameter calculator outputs, as the gradationcorrection parameter, a ratio MO/MI, where MO and MI are coordinates ofa point on the gradation correction parameter decision curve, MI beingthe maximum component value of the pixel.
 3. The image processingapparatus of claim 1, wherein the distribution extractor extracts thedistribution of a characteristic quantity of the pixels in thepredetermined period.
 4. The image processing apparatus of claim 3,wherein the characteristic quantity is a luminance value.
 5. The imageprocessing apparatus of claim 3, wherein the characteristic quantity isthe maximum component value.
 6. The image processing apparatus of claim3, wherein: the distribution extractor constructs, as said distribution,a histogram of the characteristic quantity; and the gradation-scalecorrection table generator converts the histogram to a cumulativehistogram normalized to reach a total frequency value equal to a maximumpossible value of the characteristic quantity, the gradation-scalecorrection table being a table of values substantially equal to valuesof the cumulative histogram.
 7. The image processing apparatus of claim6, wherein the cumulative histogram represents cumulative frequencies ofclasses representing successive ranges of values of the characteristicquantity, and the gradation-scale correction table stores, for eachclass among the classes, a cumulative frequency of the characteristicquantity up to a largest value in the class.
 8. The image processingapparatus of claim 7, wherein the gradation correction parametercalculator performs interpolation on successive values in thegradation-scale correction table to obtain intermediate values on thegradation correction parameter decision curve.
 9. The image processingapparatus of claim 1, wherein the gradation correction parameterdecision curve increases to a maximum value equal to a greatest possiblevalue of the maximum component value.
 10. The image processing apparatusof claim 1, wherein the gradation correction parameter decision curveincreases to a maximum value exceeding a greatest possible value of themaximum component value.
 11. The image processing apparatus of claim 1,wherein the gradation correction parameter decision curve is a brokenlime consisting of just three straight segments.
 12. The imageprocessing apparatus of claim 1, wherein the plurality of componentvalues for each pixel represent red, green, and blue.
 13. The imageprocessing apparatus of claim 1, wherein the image signal is dividedinto successive frames, and the predetermined period is one frame.